## Loading required package: lattice
## Loading required package: ggplot2
## Warning: package 'dplyr' was built under R version 3.5.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
## Warning: package 'e1071' was built under R version 3.5.2
## Warning: package 'stringr' was built under R version 3.5.2
## Warning: package 'ggmap' was built under R version 3.5.2
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
## Warning in train.default(x, y, weights = w, ...): cannnot compute class
## probabilities for regression
## k-Nearest Neighbors
##
## 3570 samples
## 2 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 3214, 3213, 3213, 3212, 3213, 3212, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 4 11.95853 0.6626195 5.588454
## 8 11.99820 0.6598352 5.698957
## 12 12.00540 0.6592070 5.773503
## 20 12.24638 0.6444100 6.037099
## 24 12.22998 0.6457030 6.068869
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 4.
## [1] 13.25783
## Warning in train.default(x, y, weights = w, ...): cannnot compute class
## probabilities for regression
## k-Nearest Neighbors
##
## 4463 samples
## 2 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 4016, 4017, 4016, 4016, 4017, 4018, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 4 16.52464 0.6265720 7.316553
## 8 15.91905 0.6499748 7.088565
## 12 16.07426 0.6423491 7.204544
## 20 16.34915 0.6301304 7.458690
## 24 16.55004 0.6207053 7.605018
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 8.
## [1] 3.041083
## [1] 13.5684
## Warning in train.default(x, y, weights = w, ...): cannnot compute class
## probabilities for regression
## k-Nearest Neighbors
##
## 4463 samples
## 2 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 4016, 4018, 4017, 4015, 4017, 4017, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 4 14.73045 0.7209207 6.265037
## 8 14.16945 0.7368472 6.113407
## 12 14.35738 0.7298605 6.260620
## 20 14.68951 0.7176696 6.563690
## 24 14.90202 0.7097077 6.701584
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 8.
## [1] 3.319044
## [1] 14.92962
## Warning in train.default(x, y, weights = w, ...): cannnot compute class
## probabilities for regression
## k-Nearest Neighbors
##
## 4463 samples
## 2 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 4015, 4017, 4017, 4017, 4018, 4017, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 4 15.39637 0.7381954 6.688300
## 8 15.07558 0.7466156 6.615964
## 12 15.12283 0.7444119 6.712862
## 20 15.54512 0.7305696 7.023439
## 24 15.77860 0.7225516 7.186679
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 8.
## [1] 3.669267
## [1] 16.43384
## Source : https://maps.googleapis.com/maps/api/staticmap?center=40.76,-73.96&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
## Warning in train.default(x, y, weights = w, ...): cannnot compute class
## probabilities for regression
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
